{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>StageID</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Relation</th>\n",
       "      <th>Raisedhands</th>\n",
       "      <th>VisitedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>ParentAnsweringSurvey</th>\n",
       "      <th>ParentschoolSatisfaction</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
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       "      <td>20</td>\n",
       "      <td>3</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Nationality PlaceofBirth     StageID GradeID SectionID Topic  \\\n",
       "0      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "1      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "2      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "3      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "4      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "\n",
       "  Semester Relation  Raisedhands  VisitedResources  AnnouncementsView  \\\n",
       "0        F   Father           15                16                  2   \n",
       "1        F   Father           20                20                  3   \n",
       "2        F   Father           10                 7                  0   \n",
       "3        F   Father           30                25                  5   \n",
       "4        F   Father           40                50                 12   \n",
       "\n",
       "   Discussion ParentAnsweringSurvey ParentschoolSatisfaction  \\\n",
       "0          20                   Yes                     Good   \n",
       "1          25                   Yes                     Good   \n",
       "2          30                    No                      Bad   \n",
       "3          35                    No                      Bad   \n",
       "4          50                    No                      Bad   \n",
       "\n",
       "  StudentAbsenceDays Class  \n",
       "0            Under-7     M  \n",
       "1            Under-7     M  \n",
       "2            Above-7     L  \n",
       "3            Above-7     L  \n",
       "4            Above-7     M  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "df=pd.read_csv('StudentPerformance.csv')\n",
    "\n",
    "df.rename(index=str,columns={\n",
    "    'gender':'Gender',\n",
    "    'NationalITy':'Nationality',\n",
    "    'raisedhands':'Raisedhands',\n",
    "    'VisITedResources':'VisitedResources'\n",
    "},inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    305\n",
       "F    175\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                      0\n",
       "Nationality                 0\n",
       "PlaceofBirth                0\n",
       "StageID                     0\n",
       "GradeID                     0\n",
       "SectionID                   0\n",
       "Topic                       0\n",
       "Semester                    0\n",
       "Relation                    0\n",
       "Raisedhands                 0\n",
       "VisitedResources            0\n",
       "AnnouncementsView           0\n",
       "Discussion                  0\n",
       "ParentAnsweringSurvey       0\n",
       "ParentschoolSatisfaction    0\n",
       "StudentAbsenceDays          0\n",
       "Class                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据是否缺失\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                      object\n",
       "Nationality                 object\n",
       "PlaceofBirth                object\n",
       "StageID                     object\n",
       "GradeID                     object\n",
       "SectionID                   object\n",
       "Topic                       object\n",
       "Semester                    object\n",
       "Relation                    object\n",
       "Raisedhands                  int64\n",
       "VisitedResources             int64\n",
       "AnnouncementsView            int64\n",
       "Discussion                   int64\n",
       "ParentAnsweringSurvey       object\n",
       "ParentschoolSatisfaction    object\n",
       "StudentAbsenceDays          object\n",
       "Class                       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据类型\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Raisedhands</th>\n",
       "      <th>VisitedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46.775000</td>\n",
       "      <td>54.797917</td>\n",
       "      <td>37.918750</td>\n",
       "      <td>43.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.779223</td>\n",
       "      <td>33.080007</td>\n",
       "      <td>26.611244</td>\n",
       "      <td>27.637735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Raisedhands  VisitedResources  AnnouncementsView  Discussion\n",
       "count   480.000000        480.000000         480.000000  480.000000\n",
       "mean     46.775000         54.797917          37.918750   43.283333\n",
       "std      30.779223         33.080007          26.611244   27.637735\n",
       "min       0.000000          0.000000           0.000000    1.000000\n",
       "25%      15.750000         20.000000          14.000000   20.000000\n",
       "50%      50.000000         65.000000          33.000000   39.000000\n",
       "75%      75.000000         84.000000          58.000000   70.000000\n",
       "max     100.000000         99.000000          98.000000   99.000000"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看常用数据\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
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       "      <th>top</th>\n",
       "      <td>M</td>\n",
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       "      <td>KuwaIT</td>\n",
       "      <td>MiddleSchool</td>\n",
       "      <td>G-02</td>\n",
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       "      <td>147</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270</td>\n",
       "      <td>292</td>\n",
       "      <td>289</td>\n",
       "      <td>211</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender Nationality PlaceofBirth       StageID GradeID SectionID Topic  \\\n",
       "count     480         480          480           480     480       480   480   \n",
       "unique      2          14           14             3      10         3    12   \n",
       "top         M          KW       KuwaIT  MiddleSchool    G-02         A    IT   \n",
       "freq      305         179          180           248     147       283    95   \n",
       "mean      NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "std       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "min       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "25%       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "50%       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "75%       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "max       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "\n",
       "       Semester Relation  Raisedhands  VisitedResources  AnnouncementsView  \\\n",
       "count       480      480   480.000000        480.000000         480.000000   \n",
       "unique        2        2          NaN               NaN                NaN   \n",
       "top           F   Father          NaN               NaN                NaN   \n",
       "freq        245      283          NaN               NaN                NaN   \n",
       "mean        NaN      NaN    46.775000         54.797917          37.918750   \n",
       "std         NaN      NaN    30.779223         33.080007          26.611244   \n",
       "min         NaN      NaN     0.000000          0.000000           0.000000   \n",
       "25%         NaN      NaN    15.750000         20.000000          14.000000   \n",
       "50%         NaN      NaN    50.000000         65.000000          33.000000   \n",
       "75%         NaN      NaN    75.000000         84.000000          58.000000   \n",
       "max         NaN      NaN   100.000000         99.000000          98.000000   \n",
       "\n",
       "        Discussion ParentAnsweringSurvey ParentschoolSatisfaction  \\\n",
       "count   480.000000                   480                      480   \n",
       "unique         NaN                     2                        2   \n",
       "top            NaN                   Yes                     Good   \n",
       "freq           NaN                   270                      292   \n",
       "mean     43.283333                   NaN                      NaN   \n",
       "std      27.637735                   NaN                      NaN   \n",
       "min       1.000000                   NaN                      NaN   \n",
       "25%      20.000000                   NaN                      NaN   \n",
       "50%      39.000000                   NaN                      NaN   \n",
       "75%      70.000000                   NaN                      NaN   \n",
       "max      99.000000                   NaN                      NaN   \n",
       "\n",
       "       StudentAbsenceDays Class  \n",
       "count                 480   480  \n",
       "unique                  2     3  \n",
       "top               Under-7     M  \n",
       "freq                  289   211  \n",
       "mean                  NaN   NaN  \n",
       "std                   NaN   NaN  \n",
       "min                   NaN   NaN  \n",
       "25%                   NaN   NaN  \n",
       "50%                   NaN   NaN  \n",
       "75%                   NaN   NaN  \n",
       "max                   NaN   NaN  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据详细信息\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Raisedhands</th>\n",
       "      <th>VisitedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46.775000</td>\n",
       "      <td>54.797917</td>\n",
       "      <td>37.918750</td>\n",
       "      <td>43.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.779223</td>\n",
       "      <td>33.080007</td>\n",
       "      <td>26.611244</td>\n",
       "      <td>27.637735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Raisedhands  VisitedResources  AnnouncementsView  Discussion\n",
       "count   480.000000        480.000000         480.000000  480.000000\n",
       "mean     46.775000         54.797917          37.918750   43.283333\n",
       "std      30.779223         33.080007          26.611244   27.637735\n",
       "min       0.000000          0.000000           0.000000    1.000000\n",
       "25%      15.750000         20.000000          14.000000   20.000000\n",
       "50%      50.000000         65.000000          33.000000   39.000000\n",
       "75%      75.000000         84.000000          58.000000   70.000000\n",
       "max     100.000000         99.000000          98.000000   99.000000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=[np.int64])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>StageID</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Relation</th>\n",
       "      <th>ParentAnsweringSurvey</th>\n",
       "      <th>ParentschoolSatisfaction</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>MiddleSchool</td>\n",
       "      <td>G-02</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>305</td>\n",
       "      <td>179</td>\n",
       "      <td>180</td>\n",
       "      <td>248</td>\n",
       "      <td>147</td>\n",
       "      <td>283</td>\n",
       "      <td>95</td>\n",
       "      <td>245</td>\n",
       "      <td>283</td>\n",
       "      <td>270</td>\n",
       "      <td>292</td>\n",
       "      <td>289</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender Nationality PlaceofBirth       StageID GradeID SectionID Topic  \\\n",
       "count     480         480          480           480     480       480   480   \n",
       "unique      2          14           14             3      10         3    12   \n",
       "top         M          KW       KuwaIT  MiddleSchool    G-02         A    IT   \n",
       "freq      305         179          180           248     147       283    95   \n",
       "\n",
       "       Semester Relation ParentAnsweringSurvey ParentschoolSatisfaction  \\\n",
       "count       480      480                   480                      480   \n",
       "unique        2        2                     2                        2   \n",
       "top           F   Father                   Yes                     Good   \n",
       "freq        245      283                   270                      292   \n",
       "\n",
       "       StudentAbsenceDays Class  \n",
       "count                 480   480  \n",
       "unique                  2     3  \n",
       "top               Under-7     M  \n",
       "freq                  289   211  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=[np.object])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    211\n",
       "H    142\n",
       "L    127\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='count'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制条形图\n",
    "import seaborn as sns\n",
    "\n",
    "#检查数据集是否平衡,order参数是绘制图像的先后顺序\n",
    "sns.countplot(x='Class',order=['L','M','H'],data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 数据没有出现极端不平衡现象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PlaceofBirth', ylabel='count'>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='PlaceofBirth',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GradeID', ylabel='count'>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='GradeID',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不同学期，学生成绩等级的数量分布差异分析\n",
    "\n",
    "结论：学生在第一学期(F)的表现比第二学期(S)差一些（在第二学期中，成绩中等的学生保持不变，但是成绩差的学生人数较少，而成绩较好的学生人数较多）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制条形图\n",
    "plt.figure(figsize=(8,6))\n",
    "sem=sns.countplot(x='Class',hue='Semester',order=['L','M','H'],data=df,palette='coolwarm')\n",
    "sem.set(xlabel='Class',ylabel='Count',title='Semester comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sem=sns.countplot(x='Class',hue='GradeID',order=['L','M','H'],data=df,palette='coolwarm')\n",
    "sem.set(xlabel='Class',ylabel='Count',title='GradeID comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "性别对学生的影响 \n",
    "\n",
    "结论：学生中男生较多，并且与女生对而言，男生低分成绩的人较多，高分成绩的人较少\n",
    "\n",
    "由图可得，女生总人数比男生少，但是女生的高分却和男生高分人数不相上下，而且女生中等人数比男生人数多，男生低分人数比女生低分人数多得多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sem=sns.countplot(x='Class',hue='Gender',order=['L','M','H'],data=df,palette='coolwarm')\n",
    "sem.set(xlabel='Class',ylabel='Count',title='Gender comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不同性别下，访问在线资源和成绩的相关性\n",
    "\n",
    "  结论：\n",
    "  1. 获得低分(L)的学生比获得中等(M)的学生访问的资源少的多\n",
    "  2. 获得高分(H)的同学几乎都访问了很多在线资源\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='VisitedResources'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(12,10)})\n",
    "sns.swarmplot(x='Class',y='VisitedResources',hue='Gender',data=df,order=['L','M','H'],size=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上课讨论积极程度和成绩的关系\n",
    "\n",
    "结论： 正相关，和实际情况符合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='Discussion'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(8,6)})\n",
    "sns.boxplot(x='Class',y='Discussion',data=df,order=['L','M','H'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrDF=df[['Raisedhands','VisitedResources','AnnouncementsView','Discussion']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制热力图\n",
    "sns.heatmap(corrDF,annot=True,cmap='YlGnBu')#annot=True参数就是显示数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                       int8\n",
       "NationalITy                  int8\n",
       "PlaceofBirth                 int8\n",
       "StageID                      int8\n",
       "GradeID                      int8\n",
       "SectionID                    int8\n",
       "Topic                        int8\n",
       "Semester                     int8\n",
       "Relation                     int8\n",
       "raisedhands                 int64\n",
       "VisITedResources            int64\n",
       "AnnouncementsView           int64\n",
       "Discussion                  int64\n",
       "ParentAnsweringSurvey        int8\n",
       "ParentschoolSatisfaction     int8\n",
       "StudentAbsenceDays           int8\n",
       "Class                        int8\n",
       "dtype: object"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.feature_selection import chi2,SelectKBest\n",
    "from scipy.stats import pearsonr\n",
    "from numpy import *\n",
    "\n",
    "df=pd.read_csv('StudentPerformance.csv')#读取数据\n",
    "\n",
    "#将object类型的列重定义为category\n",
    "df['gender']=df['gender'].astype('category')\n",
    "df['NationalITy']=df['NationalITy'].astype('category')\n",
    "df['PlaceofBirth']=df['PlaceofBirth'].astype('category')\n",
    "df['StageID']=df['StageID'].astype('category')\n",
    "df['GradeID']=df['GradeID'].astype('category')\n",
    "df['SectionID']=df['SectionID'].astype('category')\n",
    "df['Topic']=df['Topic'].astype('category')\n",
    "df['Semester']=df['Semester'].astype('category')\n",
    "df['Relation']=df['Relation'].astype('category')\n",
    "df['ParentAnsweringSurvey']=df['ParentAnsweringSurvey'].astype('category')\n",
    "df['ParentschoolSatisfaction']=df['ParentschoolSatisfaction'].astype('category')\n",
    "df['StudentAbsenceDays']=df['StudentAbsenceDays'].astype('category')\n",
    "df['Class']=df['Class'].astype('category')\n",
    "\n",
    "#独热编码\n",
    "df['gender']=df['gender'].cat.codes\n",
    "df['NationalITy']=df['NationalITy'].cat.codes\n",
    "df['PlaceofBirth']=df['PlaceofBirth'].cat.codes\n",
    "df['StageID']=df['StageID'].cat.codes\n",
    "df['GradeID']=df['GradeID'].cat.codes\n",
    "df['SectionID']=df['SectionID'].cat.codes\n",
    "df['Topic']=df['Topic'].cat.codes\n",
    "df['Semester']=df['Semester'].cat.codes\n",
    "df['Relation']=df['Relation'].cat.codes\n",
    "df['ParentAnsweringSurvey']=df['ParentAnsweringSurvey'].cat.codes\n",
    "df['ParentschoolSatisfaction']=df['ParentschoolSatisfaction'].cat.codes\n",
    "df['StudentAbsenceDays']=df['StudentAbsenceDays'].cat.codes\n",
    "df['Class']=df['Class'].cat.codes\n",
    "\n",
    "#特征工程\n",
    "X=df.drop('Class',axis=1)\n",
    "y=df['Class']\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#特征提取,pearson特征选择\n",
    "\n",
    "selector = SelectKBest(lambda X, Y: np.array(list(map(lambda x: pearsonr(x, Y)[0], X.T))).T, k=14)\n",
    "X = pd.DataFrame(selector.fit_transform(X, y),columns=np.array(X.columns)[selector.get_support(indices=True)])\n",
    "\n",
    "#X = SelectKBest(lambda X, Y: array(list(map(lambda x: pearsonr(x, Y)[0], X.T))).T,k=14).fit_transform(X, y)\n",
    "#pd.DataFrame(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>NationalITy</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>StageID</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Semester</th>\n",
       "      <th>VisITedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>ParentAnsweringSurvey</th>\n",
       "      <th>ParentschoolSatisfaction</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>25</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     gender  NationalITy  PlaceofBirth  StageID  GradeID  SectionID  Topic  \\\n",
       "0         1            4             4        2        1          0      7   \n",
       "1         1            4             4        2        1          0      7   \n",
       "2         1            4             4        2        1          0      7   \n",
       "3         1            4             4        2        1          0      7   \n",
       "4         1            4             4        2        1          0      7   \n",
       "..      ...          ...           ...      ...      ...        ...    ...   \n",
       "475       0            3             3        1        5          0      2   \n",
       "476       0            3             3        1        5          0      5   \n",
       "477       0            3             3        1        5          0      5   \n",
       "478       0            3             3        1        5          0      6   \n",
       "479       0            3             3        1        5          0      6   \n",
       "\n",
       "     Semester  VisITedResources  AnnouncementsView  Discussion  \\\n",
       "0           0                16                  2          20   \n",
       "1           0                20                  3          25   \n",
       "2           0                 7                  0          30   \n",
       "3           0                25                  5          35   \n",
       "4           0                50                 12          50   \n",
       "..        ...               ...                ...         ...   \n",
       "475         1                 4                  5           8   \n",
       "476         0                77                 14          28   \n",
       "477         1                74                 25          29   \n",
       "478         0                17                 14          57   \n",
       "479         1                14                 23          62   \n",
       "\n",
       "     ParentAnsweringSurvey  ParentschoolSatisfaction  StudentAbsenceDays  \n",
       "0                        1                         1                   1  \n",
       "1                        1                         1                   1  \n",
       "2                        0                         0                   0  \n",
       "3                        0                         0                   0  \n",
       "4                        0                         0                   0  \n",
       "..                     ...                       ...                 ...  \n",
       "475                      0                         0                   0  \n",
       "476                      0                         0                   1  \n",
       "477                      0                         0                   1  \n",
       "478                      0                         0                   0  \n",
       "479                      0                         0                   0  \n",
       "\n",
       "[480 rows x 14 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2\n",
       "1      2\n",
       "2      1\n",
       "3      1\n",
       "4      2\n",
       "      ..\n",
       "475    1\n",
       "476    2\n",
       "477    2\n",
       "478    1\n",
       "479    1\n",
       "Name: Class, Length: 480, dtype: int8"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "#生成训练集和测试集\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((384, 14), (96, 14), (384,), (96,))"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape,y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score2: 0.6979166666666666\n"
     ]
    }
   ],
   "source": [
    "model=LogisticRegression(random_state=0)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "pre=model.predict(X_test)\n",
    "\n",
    "metrics=accuracy_score(y_test,pre)\n",
    "print('accuracy score2:',metrics)"
   ]
  }
 ],
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